Novel approach to nonlinear/non-Gaussian Bayesian state estimation

doi:10.1049/ip-f-2.1993.0015

Novel approach to nonlinear/non-Gaussian Bayesian state estimation

by N J Gordon, D J Salmond, A F M Smith

View in Mendeley Desktop In your library Save reference to library

Radar and Signal Processing IEE Proceedings F (1993)

Volume: 140, Issue: 2, Publisher: IET, Pages: 107-113

ISSN: 0956375X
DOI: 10.1049/ip-f-2.1993.0015
PubMed: 19254492

Available from ieeexplore.ieee.org

or

Abstract

An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter